The Effects of Creative Culture on Real Earnings Management*
Bibliographic record
Abstract
ABSTRACT Creativity and innovation have been identified by senior executives as some of the most desired characteristics of corporate culture. Accordingly, managers strive to build these cultures within their organizations. However, research in psychology suggests that these attempts may have unintended negative consequences. In this study, I predict and find that managers in a more (versus less) innovative company culture will engage in higher levels of real earnings management (REM). I then test two construal level theory (CLT)‐based interventions designed to reduce REM. As I predict, I find that in more innovative corporate cultures an intervention that makes downside risk more salient reduces REM, but an intervention that encourages managers to consider the “big‐picture” impact of their decision reduces REM to a greater extent. Unexpectedly, I also find that the effect of the “big‐picture” intervention reverses in a less innovative corporate culture leading to an increase in REM. My findings contribute to the emerging accounting literature regarding REM. I also extend the psychology literature investigating the link between opportunistic behavior and creativity, and I also expand research into how interventions based on CLT can affect judgment and decision making in an accounting context.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".